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Deep Learning for Accelerated and Robust MRI Reconstruction: a Review
Preprint   Open access

Deep Learning for Accelerated and Robust MRI Reconstruction: a Review

Reinhard Heckel, Mathews Jacob, Akshay Chaudhari, Or Perlman and Efrat Shimron
ArXiv.org
Cornell University
04/24/2024
DOI: 10.48550/arxiv.2404.15692
url
https://doi.org/10.48550/arxiv.2404.15692View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction. It focuses on DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. These include end-to-end neural networks, pre-trained networks, generative models, and self-supervised methods. The paper also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling subtle bias. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.
 Computer Science - Learning

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